@inproceedings{kamulete2022uai,
  title    = {Test for non-negligible adverse shifts},
  author   = {Kamulete, Vathy M},
  abstract = {Statistical tests for dataset shift are susceptible to false alarms:
              they are sensitive to minor differences when there is in fact adequate
              sample coverage and predictive performance. We propose instead a
              framework to detect adverse shifts based on outlier scores,
              \texttt{D-SOS} for short. \texttt{D-SOS} holds that the new (test)
              sample is not substantively worse than the reference (training) sample,
              and not that the two are equal. The key idea is to reduce observations
              to outlier scores and compare contamination rates at varying weighted
              thresholds. Users can define what \emph{worse} means in terms of
              relevant notions of outlyingness, including proxies for predictive
              performance. Compared to tests of equal distribution, our approach is
              uniquely tailored to serve as a robust metric for model monitoring and
              data validation. We show how versatile and practical \texttt{D-SOS} is
              on a wide range of real and simulated data.}
}

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